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Ujjwal Maulik Computational Intelligence and Pattern Analysis in Biology Informatics


An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the readers understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers Chapters authored by leading researchers in CI in biology informatics. Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases. Supplementary material included: program code and relevant data sets correspond to chapters.

10125.08 РУБ

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Mourad Elloumi Pattern Recognition in Computational Molecular Biology. Techniques and Approaches


A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.

10658.13 РУБ

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Lodhi Huma M. Elements of Computational Systems Biology


Groundbreaking, long-ranging research in this emergent field that enables solutions to complex biological problems Computational systems biology is an emerging discipline that is evolving quickly due to recent advances in biology such as genome sequencing, high-throughput technologies, and the recent development of sophisticated computational methodologies. Elements of Computational Systems Biology is a comprehensive reference covering the computational frameworks and techniques needed to help research scientists and professionals in computer science, biology, chemistry, pharmaceutical science, and physics solve complex biological problems. Written by leading experts in the field, this practical resource gives detailed descriptions of core subjects, including biological network modeling, analysis, and inference; presents a measured introduction to foundational topics like genomics; and describes state-of-the-art software tools for systems biology. Offers a coordinated integrated systems view of defining and applying computational and mathematical tools and methods to solving problems in systems biology Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the implications of computational systems biology on drug design and medicine Helps reduce the gap between mathematics and biology by presenting chapters on mathematical models of biological systems Establishes solutions in computer science, biology, chemistry, and physics by presenting an in-depth description of computational methodologies for systems biology Elements of Computational Systems Biology is intended for academic/industry researchers and scientists in computer science, biology, mathematics, chemistry, physics, biotechnology, and pharmaceutical science. It is also accessible to undergraduate and graduate students in machine learning, data mining, bioinformatics, computational biology, and systems biology courses.

11500.18 РУБ

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Elloumi Mourad Algorithms in Computational Molecular Biology. Techniques, Approaches and Applications


This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.

14050.81 РУБ

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Siddhartha Bhattacharyya Hybrid Intelligence for Image Analysis and Understanding


A synergy of techniques on hybrid intelligence for real-life image analysis Hybrid Intelligence for Image Analysis and Understanding brings together research on the latest results and progress in the development of hybrid intelligent techniques for faithful image analysis and understanding. As such, the focus is on the methods of computational intelligence, with an emphasis on hybrid intelligent methods applied to image analysis and understanding. The book offers a diverse range of hybrid intelligence techniques under the umbrellas of image thresholding, image segmentation, image analysis and video analysis. Key features: Provides in-depth analysis of hybrid intelligent paradigms. Divided into self-contained chapters. Provides ample case studies, illustrations and photographs of real-life examples to illustrate findings and applications of different hybrid intelligent paradigms. Offers new solutions to recent problems in computer science, specifically in the application of hybrid intelligent techniques for image analysis and understanding, using well-known contemporary algorithms. The book is essential reading for lecturers, researchers and graduate students in electrical engineering and computer science.

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Derong Liu Fundamentals of Computational Intelligence. Neural Networks, Fuzzy Systems, and Evolutionary Computation


Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.

9135.54 РУБ

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Dehmer Matthias Analysis of Complex Networks. From Biology to Linguistics


Mathematical problems such as graph theory problems are of increasing importance for the analysis of modelling data in biomedical research such as in systems biology, neuronal network modelling etc. This book follows a new approach of including graph theory from a mathematical perspective with specific applications of graph theory in biomedical and computational sciences. The book is written by renowned experts in the field and offers valuable background information for a wide audience.

20381.39 РУБ

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Richard Brereton G. Chemometrics for Pattern Recognition


Over the past decade, pattern recognition has been one of the fastest growth points in chemometrics. This has been catalysed by the increase in capabilities of automated instruments such as LCMS, GCMS, and NMR, to name a few, to obtain large quantities of data, and, in parallel, the significant growth in applications especially in biomedical analytical chemical measurements of extracts from humans and animals, together with the increased capabilities of desktop computing. The interpretation of such multivariate datasets has required the application and development of new chemometric techniques such as pattern recognition, the focus of this work. Included within the text are: ‘Real world’ pattern recognition case studies from a wide variety of sources including biology, medicine, materials, pharmaceuticals, food, forensics and environmental science; Discussions of methods, many of which are also common in biology, biological analytical chemistry and machine learning; Common tools such as Partial Least Squares and Principal Components Analysis, as well as those that are rarely used in chemometrics such as Self Organising Maps and Support Vector Machines; Representation in full colour; Validation of models and hypothesis testing, and the underlying motivation of the methods, including how to avoid some common pitfalls. Relevant to active chemometricians and analytical scientists in industry, academia and government establishments as well as those involved in applying statistics and computational pattern recognition.

12181.44 РУБ

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Richard Brereton G. Chemometrics for Pattern Recognition


Over the past decade, pattern recognition has been one of the fastest growth points in chemometrics. This has been catalysed by the increase in capabilities of automated instruments such as LCMS, GCMS, and NMR, to name a few, to obtain large quantities of data, and, in parallel, the significant growth in applications especially in biomedical analytical chemical measurements of extracts from humans and animals, together with the increased capabilities of desktop computing. The interpretation of such multivariate datasets has required the application and development of new chemometric techniques such as pattern recognition, the focus of this work. Included within the text are: ‘Real world’ pattern recognition case studies from a wide variety of sources including biology, medicine, materials, pharmaceuticals, food, forensics and environmental science; Discussions of methods, many of which are also common in biology, biological analytical chemistry and machine learning; Common tools such as Partial Least Squares and Principal Components Analysis, as well as those that are rarely used in chemometrics such as Self Organising Maps and Support Vector Machines; Representation in full colour; Validation of models and hypothesis testing, and the underlying motivation of the methods, including how to avoid some common pitfalls. Relevant to active chemometricians and analytical scientists in industry, academia and government establishments as well as those involved in applying statistics and computational pattern recognition.

12352.36 РУБ

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Matthias Dehmer Applied Statistics for Network Biology. Methods in Systems Biology


The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed as a valuable guide for the experimental researcher as well computational biologists who often lack an appropriate background in statistical analysis.

14309.31 РУБ

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Hojjat Adeli Computational Intelligence. Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing


Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB® exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.

11038.77 РУБ

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Matthias Dehmer Computational Network Analysis with R. Applications in Biology, Medicine and Chemistry


This new title in the well-established «Quantitative Network Biology» series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

15606.54 РУБ

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Laurence Yang T. Mobile Intelligence


* Focuses on learning patterns and knowledge from data generated by mobile users and mobile technology. * Covers research and application issues in applying computational intelligence applications to mobile computing * Delivers benefits to a wide range of applications * Introduces the state of the art of computational intelligence to the mobile paradigm

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[Starwars] If the rule says there can only be two Sith Lords at the time, how come there was three of them: Darth Maul, Darth Tyrannus and Darth Sidious?

[Starwars] What if obi-wan kenobi was killed by darth maul

Would there be a darth vader? Who would have trained him?

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[#20|+18437|736] The villains in the Prequels perfectly foreshadow the rise of Darth Vader. Darth Maul was a Sith consumed by hatred, Count Dooku was a fallen Jedi, and General Grievous was a cyborg. Vader is all three of these. [/r/StarWars]

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Darth Maul - Star Wars™ Battlefront™ Heroes - Official EA Site

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Darth Maul | Jedipedia | FANDOM powered by Wikia

Darth Maul war ein dathomirischer Zabrak und Sith-Lord. Ursprünglich war er ein dathomirischer Nachtbruder und wurde Maul genannt, was in Basic soviel wie „Streithammer" bedeutet. Er wurde auf dem Planeten Iridonia von seiner Mutter Kycina im Jahre 54 VSY ausgetragen und in seiner Kindheit ...

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Darth Maul | Villains Wiki | FANDOM powered by Wikia

Darth Maul, later known as Maul, is a major antagonist in the Star Wars franchise. He was a Sith warrior and the first apprentice of Emperor Palpatine prior to the Battle of Naboo and his abandonment by his former master. A Zabrak male, Maul later resurfaced during the Clone Wars and sought...

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What Darth Maul's Solo cameo means for Star Wars

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What do you think is the best fight/battle scene in all of Star Wars media.

For me the first is the second Death Star attack on Return. It was my first foray into Star Wars and I love how it goes from ground battle to space battle to saber battle. Second would be Darth Maul. It was my first theatrical experience of Star Wars and seeing something new in the universe for the first time. Third would be the final fight in Shadows of the Empire. I liked the idea of three different factions fighting all at once. My memory is from the snes game and it's been a while so forgive...

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[#638|+520|178] The way Darth Maul uses the force in this fight scene is so underrated and one of the best in the saga [/r/StarWars]

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Maul | Wookieepedia | FANDOM powered by Wikia

Maul, formerly known as Darth Maul, was a Force-sensitive Dathomirian Zabrak male who lived during the final years of the Galactic Republic and the subsequent reign of the Galactic Empire. The son of Mother Talzin of the Nightsisters, he was taken at a young age by the Sith Lord Darth Sidious...

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In the current era of complete genome sequencing, Bioinformatics and Molecular Evolution provides an up-to-date and comprehensive introduction to bioinformatics in the context of evolutionary biology. This accessible text: provides a thorough examination of sequence analysis, biological databases, pattern recognition, and applications to genomics, microarrays, and proteomics emphasizes the theoretical and statistical methods used in bioinformatics programs in a way that is accessible to biological science students places bioinformatics in the context of evolutionary biology, including population genetics, molecular evolution, molecular phylogenetics, and their applications features end-of-chapter problems and self-tests to help students synthesize the materials and apply their understanding is accompanied by a dedicated website – www.blackwellpublishing.com/higgs – containing downloadable sequences, links to web resources, answers to self-test questions, and all artwork in downloadable format (artwork also available to instructors on CD-ROM). This important textbook will equip readers with a thorough understanding of the quantitative methods used in the analysis of molecular evolution, and will be essential reading for advanced undergraduates, graduates, and researchers in molecular biology, genetics, genomics, computational biology, and bioinformatics courses.

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Chemistry plays a very important role in the emerging field of synthetic biology. In particular, chemical synthetic biology is concerned with the synthesis of chemical structures, such as proteins, that do not exist in nature. With contributions from leading international experts, Chemical Synthetic Biology shows how chemistry underpins synthetic biology. The book is an essential guide to this fascinating new field, and will find a place on the bookshelves of researchers and students working in synthetic chemistry, synthetic and molecular biology, bioengineering, systems biology, computational genomics, and bioinformatics.

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Hitoshi Iba Evolutionary Computation in Gene Regulatory Network Research


Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. • Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) • Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications • Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology • Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.

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Luonan Chen Biomolecular Networks. Methods and Applications in Systems Biology


Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organisms form and function. This book addresses the important need for a new set of computational tools to reveal essential biological mechanisms from a systems biology approach. Readers will get comprehensive coverage of analyzing biomolecular networks in cellular systems based on available experimental data with an emphasis on the aspects of network, system, integration, and engineering. Each topic is treated in depth with specific biological problems and novel computational methods: GENE NETWORKS—Transcriptional regulation; reconstruction of gene regulatory networks; and inference of transcriptional regulatory networks PROTEIN INTERACTION NETWORKS—Prediction of protein-protein interactions; topological structure of biomolecular networks; alignment of biomolecular networks; and network-based prediction of protein function METABOLIC NETWORKS AND SIGNALING NETWORKS—Analysis, reconstruction, and applications of metabolic networks; modeling and inference of signaling networks; and other topics and new trends In addition to theoretical results and methods, many computational software tools are referenced and available from the authors Web sites. Biomolecular Networks is an indispensable reference for researchers and graduate students in bioinformatics, computational biology, systems biology, computer science, and applied mathematics.

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Volkhard Helms Principles of Computational Cell Biology. From Protein Complexes to Cellular Networks


Computational cell biology courses are increasingly obligatory for biology students around the world but of course also a must for mathematics and informatics students specializing in bioinformatics. This book, now in its second edition is geared towards both audiences. The author, Volkhard Helms, has, in addition to extensive teaching experience, a strong background in biology and informatics and knows exactly what the key points are in making the book accessible for students while still conveying in depth knowledge of the subject.About 50% of new content has been added for the new edition. Much more room is now given to statistical methods, and several new chapters address protein-DNA interactions, epigenetic modifications, and microRNAs.

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Jae Lee K. Statistical Bioinformatics. For Biomedical and Life Science Researchers


This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis Offers programming examples and datasets Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material Features supplementary materials, including datasets, links, and a statistical package available online Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.

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Basel Abu-Jamous Integrative Cluster Analysis in Bioinformatics


Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review of clustering analysis in bioinformatics from the fundamentals through to state-of-the-art techniques and applications. Key Features: Offers a contemporary review of clustering methods and applications in the field of bioinformatics, with particular emphasis on gene expression analysis Provides an excellent introduction to molecular biology with computer scientists and information engineering researchers in mind, laying out the basic biological knowledge behind the application of clustering analysis techniques in bioinformatics Explains the structure and properties of many types of high-throughput datasets commonly found in biological studies Discusses how clustering methods and their possible successors would be used to enhance the pace of biological discoveries in the future Includes a companion website hosting a selected collection of codes and links to publicly available datasets

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Ion Mandoiu Computational Methods for Next Generation Sequencing Data Analysis


Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.

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Selma Tekir Open Source Intelligence Analysis


Open sources contain publicly available informationat a low cost. The amount of open source informationan individual has access to has increaseddramatically in the 21st century. The question is howto evaluate these diversified open sources inproducing intelligence- supporting the policymakersin making their decisions by providing them with thenecessary information. To retrieve the actionableintelligence from huge amounts of open sourceinformation, and to do source validation are bigchallenges. In this book, an open source intelligence(OSINT) integrated intelligence model is proposed,and compared with the traditional intelligence model.The contemporary intelligence analyst and his OSINTmaking process is discussed. A case study of OSINT ispresented, analyzed and the analysis results aregiven. The work discusses the varying proceduresinvolved in the OSINT tasks and the need for metrics,and should be useful to information professionals,analysts, decision makers, or anyone else who needsto do analysis on data.

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Mike Nkongolo Textual Classification for Sentiment Detection. Brand Reputation Analysis on the Web using Natural Language Processing and Machine Learning


Academic Paper from the year 2018 in the subject Computer Science - Applied, University of the Witwatersrand, course: Machine learning - Artificial Intelligence - Big Data - Natural Language Processing, language: English, abstract: Cloud computing makes it possible to build scalable machine learning systems for processing massive amounts of complex data, be them structured or unstructured, real-time or historical, the so-called Big Data. Publicly available cloud computing platforms have been made available, for instance, Amazon EC2, EMR, and Google Compute Engine. More importantly, open source APIs and libraries have also been developed for ease of programming on the cloud, for instance, Cascading, Storm, Scalding, Apache Spark and Trackur. Meanwhile, computational intelligence approaches, examples of which include evolutionary computation, immune-inspired approaches, and swarm intelligence, are also employed to develop scalable machine learning and data analytics tools. In this project, we presented the sentiment-focused web crawling problem and designed a sentiment-focused web crawler frame-work for faster discovery and retrieval of sentimental context on the Web. We have developed a computational framework to perform automated reputation analysis on the Web using Natural Language Processing and Machine Learning. This paper introduces such framework and tests its performance on automated sentiment analysis for brand reputation. In addition, we proposed different strategies ...

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Frédéric Muttin Marine Coastal and Water Pollutions. Oil Spill Studies


Identifying efficient solutions to protect coastal regions from marine pollution requires expertise from a range of specialties and strategic approaches. This book gathers information on the impact of oil spills at a coastal level from different experts’ points of view, identifying synergies between domains such as mathematics, numerical modeling, mechanics, biology, economics and law. The collaborative research presented here is based on the 4th International Workshop on Anti-Pollution and Marine Coastal Water Pollution, held in La Rochelle, France at the Engineering School EIGSI, in April 2012. The areas addressed include: materials and structures (fluid-structure and capture interaction, cable and membrane equations, optimization); coastal hydrodynamics (computational fluid dynamics, numerical analysis of shallow water equations, analytical and numerical derivatives); biological impacts (biology, multivariate analysis, indicators); and economics and law (compensation costs, insurance coverage, coastal vulnerability).

5634.32 РУБ

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Frédéric Muttin Marine Coastal and Water Pollutions. Oil Spill Studies


Identifying efficient solutions to protect coastal regions from marine pollution requires expertise from a range of specialties and strategic approaches. This book gathers information on the impact of oil spills at a coastal level from different experts’ points of view, identifying synergies between domains such as mathematics, numerical modeling, mechanics, biology, economics and law. The collaborative research presented here is based on the 4th International Workshop on Anti-Pollution and Marine Coastal Water Pollution, held in La Rochelle, France at the Engineering School EIGSI, in April 2012. The areas addressed include: materials and structures (fluid-structure and capture interaction, cable and membrane equations, optimization); coastal hydrodynamics (computational fluid dynamics, numerical analysis of shallow water equations, analytical and numerical derivatives); biological impacts (biology, multivariate analysis, indicators); and economics and law (compensation costs, insurance coverage, coastal vulnerability).

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Sarkar Souradip Network on Chip Based Hardware Accelerators for Computational Biology


The focus of this work is the design and performance evaluation of Network on Chip (NoC) based multi-core hardware accelerators for computational biology. Sequence analysis and phylogenetic reconstruction are the two problems in this domain which have been addressed here. An effective approach to speed up such operations is to integrate a very high number of processing elements in a single chip so that the massive scales of fine-grain parallelism inherent in this application can be exploited efficiently. Network-on-Chip (NoC) is a very efficient method to achieve such large scale integration. Specifically, we (i) propose optimized NoC architectures for different sequence alignment algorithms that were originally designed for distributed memory parallel computers, (ii) a custom NoC architecture for solving the breakpoint phylogeny problem (iii) provide a thorough comparative evaluation of their respective performance and energy dissipation.

8652 РУБ

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U.S. Marine Corps MCWP 2-12 MAGTF - Intelligence Production and Analysis


The MCWP 2-12 provides the information needed by Marines to understand, plan, andexecute intelligence production and analysis to support MAGTF operations. It describesintelligence doctrinal fundamentals, the nature of analytical thinking, intelligencepreparation of the battlespace, and intelligence support to targeting. This publication alsodiscusses intelligence command and control, communications and information systemssupport, intelligence products and formats, planning, execution, and training.

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